Version 1
: Received: 20 May 2024 / Approved: 21 May 2024 / Online: 21 May 2024 (05:30:33 CEST)
How to cite:
Tian, Y.; Xie, C.; Wang, Y. Long Short-Term Memory Recurrent Network Architectures for Electromagnetic Field Reconstruction Based on Underground Observations. Preprints2024, 2024051347. https://doi.org/10.20944/preprints202405.1347.v1
Tian, Y.; Xie, C.; Wang, Y. Long Short-Term Memory Recurrent Network Architectures for Electromagnetic Field Reconstruction Based on Underground Observations. Preprints 2024, 2024051347. https://doi.org/10.20944/preprints202405.1347.v1
Tian, Y.; Xie, C.; Wang, Y. Long Short-Term Memory Recurrent Network Architectures for Electromagnetic Field Reconstruction Based on Underground Observations. Preprints2024, 2024051347. https://doi.org/10.20944/preprints202405.1347.v1
APA Style
Tian, Y., Xie, C., & Wang, Y. (2024). Long Short-Term Memory Recurrent Network Architectures for Electromagnetic Field Reconstruction Based on Underground Observations. Preprints. https://doi.org/10.20944/preprints202405.1347.v1
Chicago/Turabian Style
Tian, Y., Chengliang Xie and Yun Wang. 2024 "Long Short-Term Memory Recurrent Network Architectures for Electromagnetic Field Reconstruction Based on Underground Observations" Preprints. https://doi.org/10.20944/preprints202405.1347.v1
Abstract
Deep underground laboratories offer advantages for conducting high-precision observations of weak geophysical signals, benefitting from a low background noise level. It is both valuable and feasible to enhance strong, noisy ground electromagnetic (EM) field data using synchronously recorded underground EM signals, which typically exhibit a high signal-to-noise ratio. In this study, we propose an EM field reconstruction method employing a Long Short-Term Memory (LSTM) recurrent neural network with referenced deep underground EM observations. Initially, a deep learning model was developed to capture the time-varying features of underground multi-component EM fields using the LSTM recurrent neural network. Subsequently, this model was applied to process synchronously observed strong, noisy data from other conventional observation systems, such as those at surface, to achieve noise suppression through signal reconstructions. Both theoretical analysis and practical observational data suggest that the proposed method effectively suppresses noise and reconstructs clean EM signals. This method is efficient and time-saving, representing an effective approach to fully utilizing the advantages of deep underground observation data. Furthermore, this method could be extended to the processing and analysis of other geophysical data.
Environmental and Earth Sciences, Geophysics and Geology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
